{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataset import Data\n",
    "data = Data('./data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "model = tree.DecisionTreeRegressor()\n",
    "model.fit(x[:60000], y[:60000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4391., -4161.,  4817., ...,  -693.,  -673., -1425.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(model.predict(x[60000:])-y[60000:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x = [1,2,3,4,5,6]\n",
    "y = ['a','b','c','e','d','f']\n",
    "x_train, x_test, y_train, y_test, a, b = train_test_split(x, y, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zh/miniconda3/lib/python3.9/site-packages/IPython/core/events.py:89: UserWarning: Glyph 39118 (\\N{CJK UNIFIED IDEOGRAPH-98CE}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/home/zh/miniconda3/lib/python3.9/site-packages/IPython/core/events.py:89: UserWarning: Glyph 21521 (\\N{CJK UNIFIED IDEOGRAPH-5411}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/home/zh/miniconda3/lib/python3.9/site-packages/IPython/core/events.py:89: UserWarning: Glyph 36895 (\\N{CJK UNIFIED IDEOGRAPH-901F}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/home/zh/miniconda3/lib/python3.9/site-packages/IPython/core/events.py:89: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/home/zh/miniconda3/lib/python3.9/site-packages/IPython/core/events.py:89: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/home/zh/miniconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 39118 (\\N{CJK UNIFIED IDEOGRAPH-98CE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/zh/miniconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 21521 (\\N{CJK UNIFIED IDEOGRAPH-5411}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/zh/miniconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 36895 (\\N{CJK UNIFIED IDEOGRAPH-901F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/zh/miniconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 28201 (\\N{CJK UNIFIED IDEOGRAPH-6E29}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/zh/miniconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = df.iloc[:,1:4]\n",
    "import seaborn as sns\n",
    "sns.lineplot(data=d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2001\n",
       "1       2002\n",
       "2       2003\n",
       "3       2004\n",
       "4       2005\n",
       "        ... \n",
       "1435    1956\n",
       "1436    1957\n",
       "1437    1958\n",
       "1438    1959\n",
       "1439    2000\n",
       "Name: 时间HHMM, Length: 1440, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "pd.to_datetime(df.时间HHMM.astype('str')).dt.strftime(\"%H%M\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "dac8846c56bd8bb5c60b43f4d737a9f5aeedac8149ef0c6c701a72a56285111f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
